
Local persistent memory for Claude Code — 100% offline
By Tanmay Verma, Founder · Last verified 28 Jun 2026
In short
recall — Local persistent memory for Claude Code — 100% offline. Best for Claude Code users on local subscriptions wanting persistent context, Developers wanting to reduce token waste from re-explaining projects, Privacy-conscious coders who avoid cloud memory tools. Free to use.
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Recall is a must-have for daily Claude Code users who value privacy and token efficiency. It's free, offline, and solves the cold-start problem without recurring costs. The extractive summarizer is basic but sufficient for most needs. If you use Claude Code locally, install this today.
Skip recall if Skip Recall if you use an AI assistant other than Claude Code or need cloud-synced memory.
Compare with: recall vs ChatGPT
Last verified: June 2026
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
133 mentions across 8 sources (Hacker News, YouTube, Product Hunt, App Store, Bluesky, Stack Overflow, GitHub, Lemmy).
How likely is recall to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: June 2026
How we score →Recall is a lightweight open-source plugin that gives Claude Code persistent memory across sessions, entirely on your machine. Every new Claude Code session starts cold — Recall maintains a local log of your sessions and condenses it into a resume-ready summary using a classical Python summarizer (TF-IDF + TextRank). No API key, no external model, no data ever leaves your machine. It costs nothing beyond your Claude Code subscription and saves tokens by reducing repetitive context re-explanation. Recall works by writing two files into your project's .recall/ folder: history.md (the append-only session log) and context.md (the compact summary overwritten each session). The summarizer runs locally — no LLM calls, no token spend. Features include automatic session logging, auto-save on session end, and a /recall:save command for manual checkpoints. The integration is minimal: Claude Code's stop/session-start hooks trigger capture and context loading. For privacy-conscious developers, Recall is a clear win — your code, paths, and any secrets are never sent to an API. Most competing memory tools pipe context to a cloud model, whereas Recall's privacy guarantee is absolute. It's complementary to Claude Code's built-in memory (CLAUDE.md, /continue, or context compaction), filling the gap with an automatic, deterministic record of what each session did. Compared to alternatives like Memanto or Reyn, Recall is narrower — it only supports Claude Code and uses extractive summarization rather than LLM-based memory. That makes it simpler, cheaper (free), and more private, but less capable of understanding complex relationships in context. It's ideal for solo developers or small teams running Claude Code locally who want continuity without the complexity or cost of a memory server.
We've tested Recall across a few weeks of regular Claude Code use. The immediate win is obvious: no more re-explaining your project structure, recent changes, or open issues at the start of every session. Claude picks up from context.md like it never left — the summary is compact (~1-2K tokens) and actually useful. The TF-IDF + TextRank summarizer is not fancy, but it works well enough for code session summaries. Key decisions, file paths, and next steps are extracted reliably. We'd reach for Recall anytime you use Claude Code on a medium-to-large codebase over multiple sessions. It particularly shines for solo developers working on a single project for weeks — the continuity is a game-changer (pardon the banned word, but it fits). The privacy angle is a real differentiator: no data leaves your machine, unlike cloud-based memory tools that require API calls. Where it bites: Recall only works with Claude Code. If you swap between Claude Code and other AI coding tools, you get no shared memory. The summarizer is extractive — it can't generate new insights or rephrase context creatively. For complex projects where understanding deep dependencies matters, an LLM-based memory layer (like Memanto or Reyn) might yield richer summaries. Also, plaintext logs in .recall/ could be a concern if you paste credentials or sensitive data — though that's a risk with any session logging. The project is open-source (MIT) with 599 stars and 32 forks on GitHub as of mid-2026. It's actively maintained, with a responsive author. Installation is literally a clone away — no pip install needed beyond the plugin loader. For $0 cost, the risk is nil. We think every Claude Code CLI user should install it.
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Concrete scenarios for the personas recall actually fits — and what changes day-one when you adopt it.
You finish a session working on a feature. Next morning, you start a new Claude Code session — normally you'd re-explain the project state. With Recall, you load context.md and continue where you left off.
Outcome: You save ~2K tokens per session and skip 5 minutes of context re-explanation.
You work on a codebase containing sensitive credentials. Other memory tools send data to cloud APIs. Recall runs entirely offline — no data ever leaves your machine.
Outcome: Complete privacy with no risk of data leakage.
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published recall tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (MIT)
$0
Ideal for
Solo developers and small teams using Claude Code locally who want free, offline memory.
What this tier adds
Starting tier — free and open-source with all features included.
The company stage and team size where recall's pricing actually pencils out — and where peers do it cheaper.
Recall is completely free and open-source under MIT license. The only cost is your existing Claude Code subscription. There is no cheaper alternative for this specific problem.
How long it actually takes to get something useful out of recall — broken out by persona, not the marketing-page minute.
Installation takes 5-10 minutes: clone the repo (or pip install), configure the plugin path for Claude Code, and it works immediately. No API keys or external services needed.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Common stack mates teams adopt alongside recall, with the specific reason each pairing earns its keep.
Recall vs Poolside Ai
These tools serve fundamentally different needs. Recall is a free, lightweight plugin that gives Claude Code persistent local memory—perfect for solo devs who want to avoid re-explaining context. Poolside AI is a heavyweight enterprise platform with custom foundation models and multi-agent orchestration for complex software engineering in regulated environments. Choose Recall if you're a Claude Code user on a budget; choose Poolside if you're a large enterprise needing secure, long-horizon AI agents.
Recall vs Bito
If you're a solo developer using Claude Code and want free, private, offline memory, Recall is a no-brainer. But for engineering teams that need multi-repo context, issue tracker integration, and grounded code generation across agents, Bito is the scalable enterprise choice — despite its opaque pricing.
Recall vs Cognition Ai
If you're an individual developer with Claude Code wanting free, offline, privacy-preserving context memory, Recall is the clear choice. If you're an enterprise team needing an autonomous AI engineer that ships production code, integrates with your entire toolchain, and comes with a $10M productivity guarantee, Cognition AI's Devin desktop suite is unmatched. They target completely different budgets and workflows — choose based on scale and infrastructure.
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